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Abstract
Transcription factors associated with quorum sensing in P. aeruginosa are promising targets for discovering new adjuvants against infection with this pathogen. Regulation of these transcription factors offers the possibility of controlling multiple virulence factors related to them as biofilm development, proteases, hydrogen cyanide, among others. Numerous molecules have been tested against these targets, however, the keys responsible for antagonistic activity are still unknown. In this work, the structure–activity relationships of active molecules tested against LasR, PqsR, and RhlR transcription factors are analyzed in order to establish the structural characteristics associated. As part of the study, molecular complexity, scaffold, activity cliffs, and chemical space visualization analyses were conducted to find out characteristics associated with biological activity. In this study, several structural features were identified as significant for antagonist activity, highlighting molecular size and hydrogen bond acceptors.
Highlights
Infection with P. aeruginosa is a challenge in therapeutic management
These bacteria can adapt to a hostile environment, resist the immune system, and block the action of broadspectrum antibiotics such as third-generation cephalosporin, carbapenems, and others.[1,2]
One of the critical mechanisms related to the augmented antibiotic resistance of P. aeruginosa is quorum sensing (QS) which works like a communication system where each bacteria can detect an existent population in a given environment.[4]
Summary
Infection with P. aeruginosa is a challenge in therapeutic management. These bacteria can adapt to a hostile environment, resist the immune system, and block the action of broadspectrum antibiotics such as third-generation cephalosporin, carbapenems, and others.[1,2] For this reason, the World Health Organization has classi ed P. aeruginosa as a critical priority for the discovery of new drugs.[3]. Detecting new antagonists for these TFs gives innovative pathways for the P. aeruginosa infection management, the structure–activity relationship of these targets is not known yet, highlighting interesting computational studies conducted on this topic.[14–16]. For this reason, this paper proposes some relevant keys to contribute to solving this problem by searching for characteristics or structural patterns associated with agonist, antagonist activity, or inactivity. This paper proposes some relevant keys to contribute to solving this problem by searching for characteristics or structural patterns associated with agonist, antagonist activity, or inactivity To achieve this goal, a dataset was built of 289 molecules with reported biological activity in major public repositories from LasR, PqsR, and RhlR, and cheminformatics studies were performed
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